Anti-Money Laundering

Anti-money laundering fines are currently over $14B per year and rising, and compliance teams are constantly trying to balance the risk of fines with the high cost of managing false positives. Traditional approaches to anti-money laundering transaction monitoring are prone to false alarms and unable to detect sophisticated money laundering techniques. A recent PWC report estimates that 90-95% of alerts are false positives. Using Unsupervised Machine Learning, DataVisor provides the industry’s most advanced AML transaction monitoring solution that can drastically reduce false positives and false negatives compared to current TMS solutions.

The DataVisor Platform

Unsupervised Machine Learning Engine

Predict new, unknown threats without labels or training data by analyzing hundreds of millions of accounts and events simultaneously using the industry’s most advanced unsupervised learning technology.

What’s Happening in AML

As mentioned in my previous articles, traditional rule-based transaction monitoring systems (TMS) have architectural limitations which make them prone to false positives and false negatives: Naive rules create a plague of false positives that are

Keith Furst is the Founder of Data Derivatives, and has years of experience within a variety of financial institutions including Tier One wholesale banks, investment banks, foreign bank branches, commercial banks, retail banks, broker-dealers, prepaid